- Title Information
- Title
- AI Adoption, Hospital Throughput, and Employment
- Type of Resource (primo)
- technical_reports
- Abstract
- We combine 2021–2024 data on artificial intelligence (AI) adoption across U.S. shortterm general hospitals with national measures of hospital finances, volume, employment, and measured quality. Using synthetic difference-in-differences, we find that AI adoption is followed by approximately 3% higher net patient revenue, 3% higher total paid hours, and 7% higher patient volume. Total and clinical expenses also rise. By contrast, estimates for administrative expenses, administrative hours, and employee full-time equivalents are imprecise under inference clustered at the hospital-system level. Measured risk-adjusted mortality declines for several conditions, but unadjusted mortality and claims-based clinical-process measures do not show corresponding improvements, while documented severity increases. The results therefore point most clearly to operational expansion, throughput, and richer documentation; they do not establish administrative cost savings, per-unit productivity gains, or lower underlying mortality.
- Name
- Name Part
- Daniel R. Arnold
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
- Author
- Name
- Name Part
- Jonathan Cantor
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
- Author
- Name
- Name Part
- Christopher M. Whaley
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
- Author
- Origin Information
- Date Created
- 2026-08-01
- Subject (Local)
- Topic
- artificial intelligence
- Subject (Local)
- Topic
- labor substitution
- Subject (Local)
- Topic
- hospital performance
- Genre
- Working Paper
- Access Condition:
use and reproduction
- All rights reserved
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Access Condition:
restriction on access
- All Rights Reserved
- Identifier:
DOI
- 10.26300/mc71-av50